Analytics Data Engineer
Aon is in the business of better decisions
At Aon, we shape decisions for the better to protect and enrich the lives of people around the world.
As an organization, we are united through trust as one inclusive team and we are passionate about helping our colleagues and clients succeed.
What the day will look like
- Work with business, actuarial, analytical, and technical stakeholders to understand the intended analytics methodology, data inputs, business rules, and expected outputs.
- Build and/or apply analytics methodology on top of structured and semi-structured datasets using SQL, Python, and Databricks-based workflows.
- Design derived tables, views, and curated data layers that align with analytical methodology and are suitable for consumption by front-end applications, dashboards, reports, and downstream users.
- Perform statistical analysis, data profiling, exploratory analysis, and result interpretation to support methodology design and validation.
- Test methodology implementation and analytical outputs, including reconciliation, edge-case testing, data quality checks, and comparison against expected business or statistical results.
- Support the design, development, maintenance, and optimization of modern data pipelines and transformation logic using Databricks, SQL, Python, and lakehouse patterns.
- Document data mappings, transformation logic, assumptions, methodology rules, validation steps, and known limitations so that outputs are transparent and maintainable.
- Assist in support, enhancements, troubleshooting, and continuous improvement of existing data pipelines, analytical datasets, and methodology implementations.
How this opportunity is different
We are seeking a data analyst with strong modern data engineering capabilities who is keen to work at the intersection of analytics methodology, data modelling, and scalable data delivery. The right individual will have a meticulous eye for data, a strong foundation in SQL, Python, and statistical analysis, and the ability to translate business and methodological requirements into reliable datasets, derived tables, views, and analytics-ready outputs. This person will work closely with actuarial analysts, developers, data engineers, and business stakeholders to understand analytical needs, build or apply the required methodology, validate implementation results, and ensure that curated data assets can be consumed effectively by front-end applications, dashboards, and downstream analytics users.
In this role, you will work with modern lakehouse technologies such as Databricks, SQL-based analytics, Python, and structured data modelling practices, while also applying robust analytical thinking to ensure that methodology outputs are accurate, explainable, and production-ready. In addition to hands‑on delivery, this role is part of Aon’s talent growth strategy; success in this position can lead to broader opportunities in data engineering, analytics solution design, and technical leadership.
Skills and experience that will lead to success
- Strong hands‑on SQL skills, including writing complex queries, joins, aggregations, window functions, views, and analytical transformations.
- Practical Python experience for data analysis, data transformation, validation, automation, or analytical implementation.
- Experience or strong interest in working with Databricks, Spark‑based processing, notebooks, jobs, SQL warehouses, Delta tables, or lakehouse‑style data platforms.
- Ability to understand, implement, and validate analytical or statistical methodology using data, business rules, and clearly defined assumptions.
- Experience in data manipulation and data analysis skills via Excel to demonstrate methodology/algorithms
- Good understanding of statistical analysis concepts, including descriptive statistics, distributions, segmentation, outlier handling, reasonableness checks, and interpretation of analytical results.
- Experience designing analytical data models, derived tables, reporting views, or curated datasets for dashboard, application, or front‑end consumption.
- Able to translate requirements from business or methodology documents into data mappings, transformation logic, test cases, and production‑ready datasets.
- Good understanding of ETL/ELT principles, data quality checks, pipeline maintainability, performance considerations, and data lifecycle management.
- Strong attention to detail, curiosity, and willingness to learn new technologies quickly in a fast‑moving data and analytics environment.
Nice to haves:
- Experience with BI Visualization tools (PowerBI, Tableau, etc.) is an advantage
- Experience with software web development is an advantage